Imagen de Portada

Function regression using the forward forward training and inferring paradigm

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Padmani, Shivam, Joshi, Akshay
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Negative binomial regression and inference using a pre-trained transformer
por: Svensson, Valentine
Publicado: (2025)

Unifying back-propagation and forward-forward algorithms through model predictive control
por: Ren, Lianhai, et al.
Publicado: (2024)

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
por: Scholz, Vincent C., et al.
Publicado: (2024)

Mixed precision accumulation for neural network inference guided by componentwise forward error analysis
por: Arar, El-Mehdi El, et al.
Publicado: (2025)

Epistemically-guided forward-backward exploration
por: Urpí, Núria Armengol, et al.
Publicado: (2025)

The Propensity for Density in Feed-forward Models
por: Schoots, Nandi, et al.
Publicado: (2024)

An invertible generative model for forward and inverse problems
por: van Leeuwen, Tristan, et al.
Publicado: (2025)

Uncertainty propagation in feed-forward neural network models
por: Diamzon, Jeremy, et al.
Publicado: (2025)

FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
por: Gowda, Dhananjaya, et al.
Publicado: (2025)

Preferential subspace identification (PSID) with forward-backward smoothing
por: Sani, Omid G., et al.
Publicado: (2025)

Conditioning diffusion models by explicit forward-backward bridging
por: Corenflos, Adrien, et al.
Publicado: (2024)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
por: Kim, Jungho, et al.
Publicado: (2024)

Learning density ratios in causal inference using Bregman-Riesz regression
por: Hines, Oliver J., et al.
Publicado: (2025)

Conformal inference for regression on Riemannian Manifolds
por: Cholaquidis, Alejandro, et al.
Publicado: (2023)

Lookbehind-SAM: k steps back, 1 step forward
por: Mordido, Gonçalo, et al.
Publicado: (2023)

Ricci flow regularization in latent spaces for the forward learning of partial differential equations
por: Gracyk, Andrew
Publicado: (2024)

PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
por: Bai, Wensong, et al.
Publicado: (2023)

Accelerated forward-backward and Douglas-Rachford splitting dynamics
por: Ozaslan, Ibrahim K., et al.
Publicado: (2024)

Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse problems
por: Ohta, Ichiro, et al.
Publicado: (2025)

Dynamic sparsity in tree-structured feed-forward layers at scale
por: Sedghi, Reza, et al.
Publicado: (2026)

Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks
por: Franco, Nicola Rares, et al.
Publicado: (2025)

Finite-sample guarantees for data-driven forward-backward operator methods
por: Fabiani, Filippo, et al.
Publicado: (2025)

Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study
por: Krijnen, Rogier P., et al.
Publicado: (2025)

FORWARD: Dataset of a forwarder operating in rough terrain
por: Lundbäck, Mikael, et al.
Publicado: (2025)

Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
por: Zhou, Shaoqian, et al.
Publicado: (2025)

Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics
por: Chen, Ruilin
Publicado: (2025)

A Closed-form Solution for Weight Optimization in Fully-connected Feed-forward Neural Networks
por: Tomic, Slavisa, et al.
Publicado: (2024)

LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison, and Selection
por: Patel, Shivam, et al.
Publicado: (2026)

Approximate Bayesian inference for cumulative probit regression models
por: Aliverti, Emanuele
Publicado: (2025)

Learning geometry-dependent lead-field operators for forward ECG modeling
por: Dokuchaev, Arsenii, et al.
Publicado: (2026)

TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
por: Cheng, Sibo, et al.
Publicado: (2024)

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
por: Hu, Qifeng, et al.
Publicado: (2025)

Light3R-SfM: Towards Feed-forward Structure-from-Motion
por: Elflein, Sven, et al.
Publicado: (2025)

Monte Carlo inference for semiparametric Bayesian regression
por: Kowal, Daniel R., et al.
Publicado: (2023)

Linear convergence of forward-backward accelerated algorithms without knowledge of the modulus of strong convexity
por: Li, Bowen, et al.
Publicado: (2023)

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models
por: Wizadwongsa, Suttisak, et al.
Publicado: (2024)

Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers
por: Chen, Lei, et al.
Publicado: (2024)

Efficient semi-supervised inference for logistic regression under case-control studies
por: Quan, Zhuojun, et al.
Publicado: (2024)

FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
por: Wu, Jiahao, et al.
Publicado: (2024)

On minimizing the training set fill distance in machine learning regression
por: Climaco, Paolo, et al.
Publicado: (2023)